Mixture of Neural Networks: Some Experiments with the Multilayer Feedforward Architecture
A Modular Multi-Net System consist on some networks which solve partially a problem. The original problem has been decomposed into subproblems and each network focuses on solving a subproblem. The Mixture of Neural Networks consist on some expert networks which solve the subproblems and a gating network which weights the outputs of the expert networks. The expert networks and the gating network are trained all together in order to reduce the correlation among the networks and minimize the error of the system. In this paper we present the Mixture of Multilayer Feedforward (MixMF) a method based on MixNN which uses Multilayer Feedfoward networks for the expert level. Finally, we have performed a comparison among Simple Ensemble, MixNN and MixMF and the results show that MixMF is the best performing method.
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- 1.Sharkey, A.J. (ed.): Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems (1999)Google Scholar
- 4.Raviv, Y., Intratorr, N.: Bootstrapping with noise: An effective regularization technique. Connection Science, Special issue on Combining Estimators 8, 356–372 (1996)Google Scholar
- 6.Hernandez-Espinosa, C., Torres-Sospedra, J., Fernandez-Redondo, M.: New experiments on ensembles of multilayer feedforward for classification problems. In: Proceedings of International Conference on Neural Networks, IJCNN 2005, Montreal, Canada, pp. 1120–1124 (2005)Google Scholar
- 8.Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar